Overview

Dataset statistics

Number of variables14
Number of observations5695
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory623.0 KiB
Average record size in memory112.0 B

Variable types

Numeric14

Alerts

revenue is highly correlated with quantity_orders and 3 other fieldsHigh correlation
recency is highly correlated with quantity_orders and 2 other fieldsHigh correlation
quantity_orders is highly correlated with revenue and 7 other fieldsHigh correlation
quantity_items_purchased is highly correlated with revenue and 4 other fieldsHigh correlation
avg_ticket is highly correlated with revenue and 3 other fieldsHigh correlation
avg_recency is highly correlated with recency and 2 other fieldsHigh correlation
frequency is highly correlated with recency and 2 other fieldsHigh correlation
frequency_between_f_l_purchases is highly correlated with quantity_orders and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with revenue and 3 other fieldsHigh correlation
avg_unique_basked_size is highly correlated with avg_ticket and 1 other fieldsHigh correlation
quantity_items_returned is highly correlated with quantity_orders and 1 other fieldsHigh correlation
monetary_returned is highly correlated with quantity_orders and 1 other fieldsHigh correlation
revenue is highly correlated with quantity_orders and 1 other fieldsHigh correlation
recency is highly correlated with avg_recencyHigh correlation
quantity_orders is highly correlated with revenue and 1 other fieldsHigh correlation
quantity_items_purchased is highly correlated with revenue and 1 other fieldsHigh correlation
avg_ticket is highly correlated with avg_basket_size and 2 other fieldsHigh correlation
avg_recency is highly correlated with recencyHigh correlation
avg_basket_size is highly correlated with avg_ticket and 2 other fieldsHigh correlation
quantity_items_returned is highly correlated with avg_ticket and 2 other fieldsHigh correlation
monetary_returned is highly correlated with avg_ticket and 2 other fieldsHigh correlation
revenue is highly correlated with quantity_orders and 3 other fieldsHigh correlation
recency is highly correlated with avg_recency and 1 other fieldsHigh correlation
quantity_orders is highly correlated with revenue and 2 other fieldsHigh correlation
quantity_items_purchased is highly correlated with revenue and 2 other fieldsHigh correlation
avg_ticket is highly correlated with revenue and 2 other fieldsHigh correlation
avg_recency is highly correlated with recency and 1 other fieldsHigh correlation
frequency is highly correlated with recency and 1 other fieldsHigh correlation
frequency_between_f_l_purchases is highly correlated with quantity_ordersHigh correlation
avg_basket_size is highly correlated with revenue and 2 other fieldsHigh correlation
avg_unique_basked_size is highly correlated with avg_ticketHigh correlation
quantity_items_returned is highly correlated with monetary_returnedHigh correlation
monetary_returned is highly correlated with quantity_items_returnedHigh correlation
customer_id is highly correlated with recency and 2 other fieldsHigh correlation
revenue is highly correlated with quantity_orders and 5 other fieldsHigh correlation
recency is highly correlated with customer_id and 2 other fieldsHigh correlation
quantity_orders is highly correlated with revenue and 2 other fieldsHigh correlation
quantity_items_purchased is highly correlated with revenue and 4 other fieldsHigh correlation
avg_ticket is highly correlated with avg_basket_size and 2 other fieldsHigh correlation
avg_recency is highly correlated with customer_id and 2 other fieldsHigh correlation
time_in_base is highly correlated with customer_id and 2 other fieldsHigh correlation
frequency is highly correlated with revenue and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with revenue and 4 other fieldsHigh correlation
quantity_items_returned is highly correlated with revenue and 4 other fieldsHigh correlation
monetary_returned is highly correlated with revenue and 4 other fieldsHigh correlation
revenue is highly skewed (γ1 = 21.62884637) Skewed
quantity_items_purchased is highly skewed (γ1 = 23.05598553) Skewed
avg_ticket is highly skewed (γ1 = 27.82015631) Skewed
avg_basket_size is highly skewed (γ1 = 48.53682353) Skewed
quantity_items_returned is highly skewed (γ1 = 51.5242843) Skewed
monetary_returned is highly skewed (γ1 = 59.48544078) Skewed
customer_id has unique values Unique
quantity_items_returned has 4190 (73.6%) zeros Zeros
monetary_returned has 4190 (73.6%) zeros Zeros

Reproduction

Analysis started2022-04-17 18:18:49.026623
Analysis finished2022-04-17 18:19:52.052959
Duration1 minute and 3.03 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct5695
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16600.70834
Minimum12346
Maximum22709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-17T15:19:52.435927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12699.1
Q114288.5
median16229
Q318210.5
95-th percentile21731.1
Maximum22709
Range10363
Interquartile range (IQR)3922

Descriptive statistics

Standard deviation2808.223729
Coefficient of variation (CV)0.1691628858
Kurtosis-0.8211293405
Mean16600.70834
Median Absolute Deviation (MAD)1962
Skewness0.441165902
Sum94541034
Variance7886120.514
MonotonicityNot monotonic
2022-04-17T15:19:52.887503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
125721
 
< 0.1%
175341
 
< 0.1%
172051
 
< 0.1%
164121
 
< 0.1%
139231
 
< 0.1%
175201
 
< 0.1%
172011
 
< 0.1%
165631
 
< 0.1%
180421
 
< 0.1%
Other values (5685)5685
99.8%
ValueCountFrequency (%)
123461
< 0.1%
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
ValueCountFrequency (%)
227091
< 0.1%
227081
< 0.1%
227071
< 0.1%
227061
< 0.1%
227051
< 0.1%
227041
< 0.1%
227001
< 0.1%
226991
< 0.1%
226961
< 0.1%
226951
< 0.1%

revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct5449
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1803.857041
Minimum0.42
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-17T15:19:53.228359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile13.171
Q1236.24
median614.66
Q31571.11
95-th percentile5323.416
Maximum279138.02
Range279137.6
Interquartile range (IQR)1334.87

Descriptive statistics

Standard deviation7897.383597
Coefficient of variation (CV)4.378054035
Kurtosis608.1754389
Mean1803.857041
Median Absolute Deviation (MAD)480.18
Skewness21.62884637
Sum10272965.85
Variance62368667.68
MonotonicityNot monotonic
2022-04-17T15:19:53.526554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.959
 
0.2%
1.258
 
0.1%
4.958
 
0.1%
2.958
 
0.1%
1.657
 
0.1%
3.757
 
0.1%
12.757
 
0.1%
7.56
 
0.1%
4.256
 
0.1%
5.956
 
0.1%
Other values (5439)5623
98.7%
ValueCountFrequency (%)
0.421
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.844
0.1%
0.853
 
0.1%
1.071
 
< 0.1%
1.258
0.1%
1.441
 
< 0.1%
1.657
0.1%
1.691
 
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
77183.61
< 0.1%
72882.091
< 0.1%

recency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct304
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.9069359
Minimum0
Maximum373
Zeros38
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-17T15:19:53.855894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q122.5
median71
Q3200
95-th percentile338
Maximum373
Range373
Interquartile range (IQR)177.5

Descriptive statistics

Standard deviation111.6299008
Coefficient of variation (CV)0.9548612315
Kurtosis-0.643576286
Mean116.9069359
Median Absolute Deviation (MAD)61
Skewness0.8140075817
Sum665785
Variance12461.23475
MonotonicityNot monotonic
2022-04-17T15:19:54.210438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1110
 
1.9%
4105
 
1.8%
398
 
1.7%
291
 
1.6%
1086
 
1.5%
882
 
1.4%
1779
 
1.4%
979
 
1.4%
778
 
1.4%
1567
 
1.2%
Other values (294)4820
84.6%
ValueCountFrequency (%)
038
 
0.7%
1110
1.9%
291
1.6%
398
1.7%
4105
1.8%
552
0.9%
778
1.4%
882
1.4%
979
1.4%
1086
1.5%
ValueCountFrequency (%)
37323
0.4%
37222
0.4%
37117
0.3%
3694
 
0.1%
36813
0.2%
36716
0.3%
36615
0.3%
36519
0.3%
36411
0.2%
3627
 
0.1%

quantity_orders
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.469710272
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-17T15:19:55.095475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum206
Range205
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.809445663
Coefficient of variation (CV)1.962540134
Kurtosis302.566861
Mean3.469710272
Median Absolute Deviation (MAD)0
Skewness13.20109159
Sum19760
Variance46.36855023
MonotonicityNot monotonic
2022-04-17T15:19:55.721126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12871
50.4%
2827
 
14.5%
3501
 
8.8%
4395
 
6.9%
5236
 
4.1%
6173
 
3.0%
7139
 
2.4%
898
 
1.7%
968
 
1.2%
1055
 
1.0%
Other values (46)332
 
5.8%
ValueCountFrequency (%)
12871
50.4%
2827
 
14.5%
3501
 
8.8%
4395
 
6.9%
5236
 
4.1%
6173
 
3.0%
7139
 
2.4%
898
 
1.7%
968
 
1.2%
1055
 
1.0%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
911
< 0.1%
901
< 0.1%
861
< 0.1%
721
< 0.1%
622
< 0.1%
601
< 0.1%

quantity_items_purchased
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1842
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean978.6463565
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-17T15:19:56.094912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q1106
median317
Q3805.5
95-th percentile2943.3
Maximum196844
Range196843
Interquartile range (IQR)699.5

Descriptive statistics

Standard deviation4429.032218
Coefficient of variation (CV)4.525671801
Kurtosis785.3589653
Mean978.6463565
Median Absolute Deviation (MAD)253
Skewness23.05598553
Sum5573391
Variance19616326.39
MonotonicityNot monotonic
2022-04-17T15:19:56.449709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1113
 
2.0%
273
 
1.3%
351
 
0.9%
449
 
0.9%
535
 
0.6%
629
 
0.5%
1225
 
0.4%
8822
 
0.4%
7221
 
0.4%
720
 
0.4%
Other values (1832)5257
92.3%
ValueCountFrequency (%)
1113
2.0%
273
1.3%
351
0.9%
449
0.9%
535
 
0.6%
629
 
0.5%
720
 
0.4%
818
 
0.3%
97
 
0.1%
1017
 
0.3%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
802631
< 0.1%
773731
< 0.1%
742151
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct5454
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean582.1710525
Minimum0.42
Maximum84236.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-17T15:19:56.960771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile12.835
Q1158.975
median297.56
Q3486.8193956
95-th percentile1842.344
Maximum84236.25
Range84235.83
Interquartile range (IQR)327.8443956

Descriptive statistics

Standard deviation2040.79593
Coefficient of variation (CV)3.505491936
Kurtosis987.7715332
Mean582.1710525
Median Absolute Deviation (MAD)152.36
Skewness27.82015631
Sum3315464.144
Variance4164848.027
MonotonicityNot monotonic
2022-04-17T15:19:57.214626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.959
 
0.2%
1.258
 
0.1%
2.958
 
0.1%
4.958
 
0.1%
12.757
 
0.1%
3.757
 
0.1%
1.657
 
0.1%
4.256
 
0.1%
5.956
 
0.1%
7.56
 
0.1%
Other values (5444)5623
98.7%
ValueCountFrequency (%)
0.421
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.844
0.1%
0.853
 
0.1%
1.071
 
< 0.1%
1.258
0.1%
1.441
 
< 0.1%
1.657
0.1%
1.691
 
< 0.1%
ValueCountFrequency (%)
84236.251
< 0.1%
77183.61
< 0.1%
52940.941
< 0.1%
50653.911
< 0.1%
21389.61
< 0.1%
18745.861
< 0.1%
14855.531
< 0.1%
14844.766671
< 0.1%
13305.51
< 0.1%
12681.581
< 0.1%

avg_recency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1181
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.0251413
Minimum0
Maximum373
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-17T15:19:57.501463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q144.125
median86
Q3184
95-th percentile336.3
Maximum373
Range373
Interquartile range (IQR)139.875

Descriptive statistics

Standard deviation101.8129872
Coefficient of variation (CV)0.8209060368
Kurtosis-0.2554173381
Mean124.0251413
Median Absolute Deviation (MAD)55.33333333
Skewness0.9372147346
Sum706323.1796
Variance10365.88436
MonotonicityNot monotonic
2022-04-17T15:19:57.818282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6032
 
0.6%
5331
 
0.5%
21330
 
0.5%
35330
 
0.5%
18429
 
0.5%
4628
 
0.5%
6427
 
0.5%
2827
 
0.5%
7726
 
0.5%
15425
 
0.4%
Other values (1171)5410
95.0%
ValueCountFrequency (%)
04
 
0.1%
111
0.2%
27
 
0.1%
2.8473282441
 
< 0.1%
313
0.2%
3.3008849561
 
< 0.1%
3.3303571431
 
< 0.1%
3.3333333331
 
< 0.1%
418
0.3%
4.1444444441
 
< 0.1%
ValueCountFrequency (%)
37323
0.4%
37221
0.4%
37117
0.3%
3694
 
0.1%
36813
0.2%
36716
0.3%
36614
0.2%
36519
0.3%
36411
0.2%
3627
 
0.1%

time_in_base
Real number (ℝ≥0)

HIGH CORRELATION

Distinct305
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217.2491659
Minimum1
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-17T15:19:58.182073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25
Q1110
median239
Q3319
95-th percentile370
Maximum374
Range373
Interquartile range (IQR)209

Descriptive statistics

Standard deviation116.5840834
Coefficient of variation (CV)0.5366376572
Kurtosis-1.233603755
Mean217.2491659
Median Absolute Deviation (MAD)96
Skewness-0.2947870121
Sum1237234
Variance13591.84851
MonotonicityNot monotonic
2022-04-17T15:19:58.479906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
374101
 
1.8%
37397
 
1.7%
36788
 
1.5%
36978
 
1.4%
36676
 
1.3%
37070
 
1.2%
35966
 
1.2%
36861
 
1.1%
37257
 
1.0%
36046
 
0.8%
Other values (295)4955
87.0%
ValueCountFrequency (%)
14
 
0.1%
211
0.2%
37
 
0.1%
413
0.2%
518
0.3%
69
0.2%
813
0.2%
96
 
0.1%
1014
0.2%
1121
0.4%
ValueCountFrequency (%)
374101
1.8%
37397
1.7%
37257
1.0%
37070
1.2%
36978
1.4%
36861
1.1%
36788
1.5%
36676
1.3%
36545
0.8%
36332
 
0.6%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1222
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02306421228
Minimum0.002673796791
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-17T15:19:58.886671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.002673796791
5-th percentile0.00296735905
Q10.005449591281
median0.01201201201
Q30.02379800602
95-th percentile0.06879962081
Maximum1
Range0.9973262032
Interquartile range (IQR)0.01834841474

Descriptive statistics

Standard deviation0.04828311569
Coefficient of variation (CV)2.093421405
Kurtosis173.325764
Mean0.02306421228
Median Absolute Deviation (MAD)0.00750018311
Skewness10.79745302
Sum131.3506889
Variance0.00233125926
MonotonicityNot monotonic
2022-04-17T15:19:59.462449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0185185185237
 
0.6%
0.00540540540532
 
0.6%
0.0163934426231
 
0.5%
0.00282485875730
 
0.5%
0.00467289719630
 
0.5%
0.0153846153829
 
0.5%
0.0526315789529
 
0.5%
0.0192307692328
 
0.5%
0.02527
 
0.5%
0.0454545454526
 
0.5%
Other values (1212)5396
94.7%
ValueCountFrequency (%)
0.00267379679122
0.4%
0.00268096514721
0.4%
0.00268817204317
0.3%
0.0027027027033
 
0.1%
0.002710027113
0.2%
0.00271739130416
0.3%
0.0027247956414
0.2%
0.00273224043719
0.3%
0.00273972602711
0.2%
0.0027548209377
 
0.1%
ValueCountFrequency (%)
15
0.1%
0.5508021391
 
< 0.1%
0.53208556151
 
< 0.1%
0.511
0.2%
0.41
 
< 0.1%
0.33333333336
0.1%
0.33155080211
 
< 0.1%
0.31578947371
 
< 0.1%
0.27272727272
 
< 0.1%
0.26216216221
 
< 0.1%

frequency_between_f_l_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1225
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5475706259
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-17T15:19:59.911889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.01102941176
Q10.02492211838
median1
Q31
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.9750778816

Descriptive statistics

Standard deviation0.5505967909
Coefficient of variation (CV)1.005526529
Kurtosis138.7856997
Mean0.5475706259
Median Absolute Deviation (MAD)0
Skewness4.851371477
Sum3118.414715
Variance0.3031568261
MonotonicityNot monotonic
2022-04-17T15:20:00.445273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12879
50.6%
248
 
0.8%
0.062518
 
0.3%
0.0277777777817
 
0.3%
0.0238095238116
 
0.3%
0.0909090909115
 
0.3%
0.0833333333315
 
0.3%
0.0344827586214
 
0.2%
0.0294117647114
 
0.2%
0.0192307692313
 
0.2%
Other values (1215)2646
46.5%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
< 0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
< 0.1%
0.005665722381
 
< 0.1%
0.0056818181822
< 0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
41
 
< 0.1%
35
 
0.1%
248
 
0.8%
1.1428571431
 
< 0.1%
12879
50.6%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2371
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.271079
Minimum1
Maximum74215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-17T15:20:01.450737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q175
median152
Q3290.7083333
95-th percentile734.3
Maximum74215
Range74214
Interquartile range (IQR)215.7083333

Descriptive statistics

Standard deviation1199.192546
Coefficient of variation (CV)4.470077617
Kurtosis2768.431965
Mean268.271079
Median Absolute Deviation (MAD)97
Skewness48.53682353
Sum1527803.795
Variance1438062.761
MonotonicityNot monotonic
2022-04-17T15:20:01.691094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1114
 
2.0%
272
 
1.3%
351
 
0.9%
449
 
0.9%
535
 
0.6%
629
 
0.5%
1226
 
0.5%
7222
 
0.4%
10022
 
0.4%
8821
 
0.4%
Other values (2361)5254
92.3%
ValueCountFrequency (%)
1114
2.0%
272
1.3%
351
0.9%
3.3333333331
 
< 0.1%
449
0.9%
535
 
0.6%
5.3333333331
 
< 0.1%
5.6666666671
 
< 0.1%
629
 
0.5%
6.1428571431
 
< 0.1%
ValueCountFrequency (%)
742151
< 0.1%
40498.51
< 0.1%
141491
< 0.1%
139561
< 0.1%
78241
< 0.1%
6009.3333331
< 0.1%
59631
< 0.1%
51971
< 0.1%
43001
< 0.1%
42821
< 0.1%

avg_unique_basked_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1256
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.99261783
Minimum1
Maximum1113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-17T15:20:02.116693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.333333333
Q19
median18
Q335.65833333
95-th percentile175.3
Maximum1113
Range1112
Interquartile range (IQR)26.65833333

Descriptive statistics

Standard deviation77.01107847
Coefficient of variation (CV)1.925632345
Kurtosis32.29813214
Mean39.99261783
Median Absolute Deviation (MAD)11.28571429
Skewness4.995726188
Sum227757.9586
Variance5930.706208
MonotonicityNot monotonic
2022-04-17T15:20:02.379995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1282
 
5.0%
2160
 
2.8%
3115
 
2.0%
13108
 
1.9%
10103
 
1.8%
999
 
1.7%
695
 
1.7%
595
 
1.7%
492
 
1.6%
1191
 
1.6%
Other values (1246)4455
78.2%
ValueCountFrequency (%)
1282
5.0%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
< 0.1%
1.58
 
0.1%
1.5681818181
 
< 0.1%
1.5714285711
 
< 0.1%
1.6666666674
 
0.1%
1.8333333331
 
< 0.1%
2160
2.8%
ValueCountFrequency (%)
11131
< 0.1%
7481
< 0.1%
7301
< 0.1%
7201
< 0.1%
7041
< 0.1%
6861
< 0.1%
6751
< 0.1%
6741
< 0.1%
6611
< 0.1%
6501
< 0.1%

quantity_items_returned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct216
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.08446005
Minimum0
Maximum80995
Zeros4190
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-17T15:20:02.618747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile39
Maximum80995
Range80995
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1474.760408
Coefficient of variation (CV)31.32159541
Kurtosis2718.145124
Mean47.08446005
Median Absolute Deviation (MAD)0
Skewness51.5242843
Sum268146
Variance2174918.261
MonotonicityNot monotonic
2022-04-17T15:20:02.840052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04190
73.6%
1169
 
3.0%
2150
 
2.6%
3105
 
1.8%
489
 
1.6%
678
 
1.4%
561
 
1.1%
1252
 
0.9%
744
 
0.8%
843
 
0.8%
Other values (206)714
 
12.5%
ValueCountFrequency (%)
04190
73.6%
1169
 
3.0%
2150
 
2.6%
3105
 
1.8%
489
 
1.6%
561
 
1.1%
678
 
1.4%
744
 
0.8%
843
 
0.8%
941
 
0.7%
ValueCountFrequency (%)
809951
< 0.1%
742151
< 0.1%
93601
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%

monetary_returned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1087
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.62016681
Minimum0
Maximum168469.6
Zeros4190
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-17T15:20:03.089560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.925
95-th percentile107.4
Maximum168469.6
Range168469.6
Interquartile range (IQR)3.925

Descriptive statistics

Standard deviation2493.554888
Coefficient of variation (CV)30.18094714
Kurtosis3815.139979
Mean82.62016681
Median Absolute Deviation (MAD)0
Skewness59.48544078
Sum470521.85
Variance6217815.978
MonotonicityNot monotonic
2022-04-17T15:20:03.416987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04190
73.6%
12.7520
 
0.4%
4.9519
 
0.3%
9.9517
 
0.3%
1517
 
0.3%
5.912
 
0.2%
25.511
 
0.2%
4.2510
 
0.2%
3.759
 
0.2%
19.98
 
0.1%
Other values (1077)1382
 
24.3%
ValueCountFrequency (%)
04190
73.6%
0.422
 
< 0.1%
0.651
 
< 0.1%
0.951
 
< 0.1%
1.254
 
0.1%
1.454
 
0.1%
1.641
 
< 0.1%
1.655
 
0.1%
1.72
 
< 0.1%
1.791
 
< 0.1%
ValueCountFrequency (%)
168469.61
< 0.1%
77183.61
< 0.1%
22998.41
< 0.1%
14688.241
< 0.1%
8511.151
< 0.1%
7443.591
< 0.1%
5228.41
< 0.1%
4815.261
< 0.1%
4814.741
< 0.1%
4486.241
< 0.1%

Interactions

2022-04-17T15:19:45.700115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:18:56.238943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:01.096360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:05.981670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:09.452686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:12.834117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:16.911159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:20.174933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:23.358473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:27.150255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:30.750396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:34.812085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:38.085103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:42.373596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:45.928267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:18:56.470551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:01.495321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:06.197895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:09.665705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:13.117953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:17.124038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:20.401633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:23.577621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:27.366271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:31.350511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:35.040103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:38.307766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:42.595467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:46.149568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:18:56.684450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:01.827299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:06.421144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:09.910310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:13.361995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:17.345911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:20.610491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:23.795506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:27.579608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:31.643348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:35.269971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:38.576613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:42.832334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:46.469659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:18:57.038229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:02.136674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:06.660262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:10.152584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:13.878828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:17.581079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:20.842849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:24.020638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:27.835740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:31.916449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:35.511832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:39.165203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:43.067198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:46.830674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:18:57.439602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:02.386720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:06.870483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:10.381432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:14.090690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:17.822730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:21.046867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:24.229779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:28.085014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:32.141978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:35.737116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:39.513585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:43.284772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:47.151724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:18:57.921520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:02.645574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:07.130753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:10.637285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:14.364673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:18.061026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:21.290977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:24.467800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:28.330851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:32.418149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:35.980358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:39.766441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:43.544753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:47.419851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:18:58.232519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:03.449389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:07.370864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:10.883203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:14.593551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:18.263066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:21.491176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:24.674666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:28.529029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:32.627189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:36.199251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:39.973329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:43.774744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:47.714825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:18:58.533172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:03.700371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:07.627700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:11.117070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:14.847399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:18.479093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:21.722190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:24.903552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:28.754086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:32.961001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:36.425370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:40.209934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:44.027599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:48.026274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:18:58.848121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:03.998080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:07.903686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:11.366930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:15.148134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:18.706113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:21.949191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:25.550315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:28.971099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:33.202863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:36.656433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:40.443940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:44.273628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:48.351836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:18:59.164273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:04.372205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:08.128855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:11.599811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:15.382001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:18.914001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:22.181058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:25.813463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:29.176998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:33.435730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:36.876609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:40.701144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:44.492506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:48.714948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:18:59.521314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:04.752684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:08.415770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:11.906363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:15.645216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:19.152873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:22.430470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:26.149504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:29.412969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:33.699576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:37.111600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:41.335489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:44.742360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:49.142260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:18:59.939075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:05.096715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:08.688727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:12.144372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:15.938390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:19.375758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:22.656341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:26.449094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:29.667784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:33.943456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:37.356746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:41.592636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:44.981361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:49.414476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:00.219915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:05.373240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:08.933733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:12.353268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:16.253138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:19.661307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:22.880210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:26.672099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:30.051471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:34.179303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:37.582763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:41.875092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:45.209234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:49.741559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:00.665029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:05.706225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:09.197694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:12.596113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:16.546312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:19.953634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:23.117348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:26.916128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:30.349486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:34.536098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:37.827847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:42.137593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-17T15:19:45.466227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-17T15:20:03.673669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-17T15:20:04.174339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-17T15:20:04.553391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-17T15:20:05.097943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-17T15:19:50.258191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-17T15:19:51.418004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

customer_idrevenuerecencyquantity_ordersquantity_items_purchasedavg_ticketavg_recencytime_in_basefrequencyfrequency_between_f_l_purchasesavg_basket_sizeavg_unique_basked_sizequantity_items_returnedmonetary_returned
0178505391.21372341733158.565000186.5000003740.09090917.00000050.9705888.73529440102.58
1130473232.595691390359.17666753.2857143740.0240640.028302154.44444419.00000035143.49
2125836705.382155028447.02533324.8666673740.0401070.040323335.20000015.4666675076.04
313748948.25955439189.65000093.2500003740.0133690.01792187.8000005.60000000.00
415100876.00333380292.000000124.3333333740.0080210.07317126.6666671.00000022240.90
5152914623.3025142102330.23571426.6428573740.0374330.040115150.1428577.2857142971.79
6146885630.877213621268.13666718.6500003740.0561500.057221172.42857115.571429399523.49
7178095411.9116122057450.99250037.3000003740.0320860.033520171.4166675.0833334167.06
81531160767.9009138194667.7791214.1444443740.2433160.243316419.71428626.1428574741348.56
9160982005.63877613286.51857153.2857143740.0187170.02439087.5714299.57142900.00

Last rows

customer_idrevenuerecencyquantity_ordersquantity_items_purchasedavg_ticketavg_recencytime_in_basefrequencyfrequency_between_f_l_purchasesavg_basket_sizeavg_unique_basked_sizequantity_items_returnedmonetary_returned
5685226956083.951118526083.951.020.51.01852.0675.000.0
5686226967150.071121507150.071.020.51.02150.0748.000.0
5687226993686.80116913686.801.020.51.0691.0203.000.0
5688227004839.421110744839.421.020.51.01074.062.000.0
56892270417.90111417.901.020.51.014.07.000.0
5690227053.351123.351.020.51.02.02.000.0
5691227065699.001117475699.001.020.51.01747.0634.000.0
5692227076756.060120106756.060.011.01.02010.0730.000.0
5693227083217.20016543217.200.011.01.0654.059.000.0
5694227093950.72017313950.720.011.01.0731.0217.000.0